EISeg: An Efficient Interactive Segmentation Tool based on PaddlePaddle
This tool addresses the annotation bottleneck for researchers and practitioners in domains like remote sensing and medical imaging, offering a practical improvement over existing manual methods.
The paper tackles the tedious and time-consuming manual annotation required for training deep learning segmentation models by introducing EISeg, an efficient interactive segmentation tool that generates highly accurate masks with only a few clicks.
In recent years, the rapid development of deep learning has brought great advancements to image and video segmentation methods based on neural networks. However, to unleash the full potential of such models, large numbers of high-quality annotated images are necessary for model training. Currently, many widely used open-source image segmentation software relies heavily on manual annotation which is tedious and time-consuming. In this work, we introduce EISeg, an Efficient Interactive SEGmentation annotation tool that can drastically improve image segmentation annotation efficiency, generating highly accurate segmentation masks with only a few clicks. We also provide various domain-specific models for remote sensing, medical imaging, industrial quality inspections, human segmentation, and temporal aware models for video segmentation. The source code for our algorithm and user interface are available at: https://github.com/PaddlePaddle/PaddleSeg.